thin_base function

Base binomial thinning function.

Given a matrix of counts ($Y$) where $log_2(E[Y]) = Q$, a design matrix ($X$), and a matrix of coefficients ($B$), thin_diff will generate a new matrix of counts such that $log_2(E[Y]) = BX' + u1' + Q$, where $u$ is some vector of intercept coefficients. This function is used by all other thinning functions. The method is described in detail in Gerard (2020).

thin_base(mat, designmat, coefmat, relative = TRUE, type = c("thin", "mult"))

Arguments

• mat: A numeric matrix of RNA-seq counts. The rows index the genes and the columns index the samples.
• designmat: A design matrix. The rows index the samples and the columns index the variables. The intercept should not be included.
• coefmat: A matrix of coefficients. The rows index the genes and the columns index the samples.
• relative: A logical. Should we apply relative thinning (TRUE) or absolute thinning (FALSE). Only experts should change the default.
• type: Should we apply binomial thinning (type = "thin") or just naive multiplication of the counts (type = "mult"). You should always have this set to "thin".

Returns

A matrix of new RNA-seq read-counts. This matrix has the signal added from designmat and coefmat.

Examples

## Simulate data from given matrix of counts
## In practice, you would obtain Y from a real dataset, not simulate it.
set.seed(1)
nsamp <- 10
ngene <- 1000
Y <- matrix(stats::rpois(nsamp * ngene, lambda = 100), nrow = ngene)
X <- matrix(rep(c(0, 1), length.out = nsamp))
B <- matrix(seq(3, 0, length.out = ngene))
Ynew <- thin_base(mat = Y, designmat = X, coefmat = B)

## Demonstrate how the log2 effect size is B
Bhat <- coefficients(lm(t(log2(Ynew)) ~ X))["X", ]
plot(B, Bhat, xlab = "Coefficients", ylab = "Coefficient Estimates")
abline(0, 1, col = 2, lwd = 2)

References

• Gerard, D (2020). "Data-based RNA-seq simulations by binomial thinning." BMC Bioinformatics. 21(1), 206. tools:::Rd_expr_doi("10.1186/s12859-020-3450-9") .

See Also

• select_counts: For subsampling the rows and columns of your real RNA-seq count matrix prior to applying binomial thinning.
• thin_diff: For the function most users should be using for general-purpose binomial thinning.
• thin_2group: For the specific application of thinning in the two-group model.
• thin_lib: For the specific application of library size thinning.
• thin_gene: For the specific application of total gene expression thinning.
• thin_all: For the specific application of thinning all counts uniformly.

Author(s)

David Gerard